{"id":"W2155042384","doi":"","title":"Optimization with EM and expectation-conjugate-gradient","year":2003,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Expectation–maximization algorithm; Convergence (economics); Conjugate gradient method; Latent variable; Mathematical optimization; Computer science; Maximization; Maximum likelihood; Nonlinear conjugate gradient method; Estimation theory; Conjugate residual method; Variable (mathematics); Applied mathematics; Latent variable model; Gradient method; Gradient descent; Mathematics; Algorithm; Artificial intelligence; Statistics; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003702932,0.001627269,0.001256304,0.001272398,0.0004204495,0.001602296,0.002142848,0.002572886,0.003873779],"category_scores_gemma":[0.02127139,0.0008461294,0.0008700316,0.00189561,0.00221208,0.00331906,0.002828229,0.00278669,0.002336896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008102862,"about_ca_system_score_gemma":0.0008908627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007590041,"about_ca_topic_score_gemma":0.0008298513,"domain_scores_codex":[0.9975043,0.001576238,0.0001040345,0.0003426108,0.0004009134,0.00007187706],"domain_scores_gemma":[0.9963506,0.002435409,0.0003097559,0.000555407,0.0002783894,0.00007040048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008156816,0.00007796192,0.000608119,0.0003122986,0.0001358637,0.0001214161,0.0001218368,0.3365129,0.001461605,0.498313,0.005380813,0.1568727],"study_design_scores_gemma":[0.00002140131,0.00004164832,0.0001881337,0.00003311136,0.00001643281,0.000100683,0.000009255274,0.8099358,0.001969875,0.1809299,0.006727416,0.0000263369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004930582,0.0002285399,0.9982764,0.0001053313,0.00002565438,0.00001304027,0.00001556522,0.0001111706,0.000731348],"genre_scores_gemma":[0.05390745,0.001006281,0.9413748,0.00019974,0.0001891668,0.0002715104,0.0001561415,0.0001859639,0.002708989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003873779,"threshold_uncertainty_score":0.01958323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04663036366903674,"score_gpt":0.3180362623723024,"score_spread":0.2714058987032657,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}